dv-query Skill
Bulk reads, multi-page iteration, and analytics over Dataverse data. Use when the user wants to read, list, filter, aggregate, group, join, or analyze records — including pandas DataFrame workflows and notebook exploration. Published by microsoft in Dataverse-skills.
Decision snapshot
Is this a fit?
Database workflows, Data analysis, Includes SKILL.md, Reusable instructions
Compatibility not yet detected.
Permission behavior not yet detected.
Copy skill directory
2 months ago · MIT license
No specific cautions were detected. Review the source and requested permissions before installing.
What is dv-query Skill?
Bulk reads, multi-page iteration, and analytics over Dataverse data. Use when the user wants to read, list, filter, aggregate, group, join, or analyze records — including pandas DataFrame workflows and notebook exploration. Published by microsoft in Dataverse-skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Data analysis
- Database workflows use cases
- Data analysis use cases
Declared skill metadata
- Source file: .github/plugins/dataverse/skills/dv-query/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use dv-query Skill
- Use it for database workflows.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/Dataverse-skills
- Skill file: .github/plugins/dataverse/skills/dv-query/SKILL.md
What it does
Bulk reads, multi-page iteration, and analytics over Dataverse data. Use when the user wants to read, list, filter, aggregate, group, join, or analyze records — including pandas DataFrame workflows and notebook exploration.
Skill instructions
Skill: Query — Read and Analyze Dataverse Records This skill uses Python and the Dataverse CLI. Do not use Node.js, JavaScript, or any other language for Dataverse scripting. See the overview skill's Hard Rules. Reads: prefer a managed surface, choose by shape Fast path for simple reads: If dataverse auth who shows an active profile, skip workspace setup and query directly with the CLI examples below. No .env, auth.py, pip install, or PAC needed for data reads. Pick MCP, the Dataverse CLI, or the SDK by the shape of the read — all three handle auth and retry (see the routing table below and the overview's Tool Capabilities / Hard Rule 2). MCP fits small, interactive reads; the CLI fits headless one-liners (OData, SQL, count); the SDK fits bulk iteration and analytics. For $apply aggregation and N:N $expand, prefer client.query.fetchxml() (aggregates + link-entity) or the managed dataverse api escape hatch; reach for hand-rolled urllib/gettoken() only to stay in-process inside a tight P
Verified compatibility and discovery
Frequently asked questions
What is dv-query?
dv-query is a open-source AI agent skill with Copy skill directory. Bulk reads, multi-page iteration, and analytics over Dataverse data. Use when the user wants to read, list, filter, aggregate, group, join, or analyze records — including pandas DataFrame workflows and notebook.
Who is dv-query best for?
dv-query is best for reusing agent instructions, scripts, and references, database workflows, data analysis workflows.
How do I install dv-query?
Install or run dv-query using Copy skill directory. Check dv-query for the latest setup command.
Is dv-query actively maintained?
dv-query may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
